Sensitivity is better for the frequentist GLASSO than for the Bayesian GLASSO, though Bayesian GLASSO performance is usually close¶

Claim · All claims
Evidence · 1 study · causal r2 · q1
Currency · newest study 2022 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2025: tool and model accuracy changes quickly

Subclaims¶

q1 i? In the simulations, sensitivity was better for the frequentist GLASSO, but the Bayesian GLASSO's performance was usually close. → Joran Jongerling 2022

Evidence¶

Joran Jongerling 2022¶

Joran Jongerling, Sacha Epskamp, Donald Williams. (2022). Bayesian uncertainty estimation for Gaussian graphical models and centrality indices. Multivariate Behavioral Research. https://www.nwea.org/research/publication/bayesian-uncertainty-estimation-for-gaussian-graphical-models-and-centrality-indices/

q1 · i? · causal · r2

Simulation results reported in the abstract: "Sensitivity was better for the frequentist GLASSO", with the Bayesian GLASSO usually close. No sensitivity values or effect sizes are printed in the available text.

"Sensitivity was better for the frequentist GLASSO, but performance of the Bayesian GLASSO is usually close."

Discussion¶

Related Claims¶

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• In simulations, Bayesian GLASSO estimation outperforms frequentist GLASSO on bias in edge weights, centrality measures, correlation between estimated and true partial correlations, and specificity — related
• The frequentist graphical LASSO has difficulty estimating centrality indices and uncertainty in these measures in symptom networks — related
• Regularization-based graphical modeling has key drawbacks, including difficulty obtaining valid parameter uncertainty and inflated false positive rates — related